Drug Safety[JOURNAL]
Sun
200 papers
Stakeholders' Perspectives Toward the Use of Patient Registry Data for Decision-Making on Medicines: A Cross-Sectional Survey.
Windfuhr F, de Vries ST, Melinder M … +6 more , Dahlqvist T, Almeida D, Sepodes B, Torre C, Wettermark B, Mol PGM
Drug Saf · 2025 Jul · PMID 39987538 · Full text
BACKGROUND: The use of patient registries in regulatory, health technology assessment (HTA), and payer decision-making has gained increasing attention in recent years. Stakeholders' perspectives toward the use of registr...
BACKGROUND: The use of patient registries in regulatory, health technology assessment (HTA), and payer decision-making has gained increasing attention in recent years. Stakeholders' perspectives toward the use of registry-based real-world evidence (RWE) are unknown. OBJECTIVES: The purpose of this study was to assess stakeholders' perspectives toward the use of RWE from patient registries in decision-making on medicines and explore factors influencing their intention to use registry data in the future. METHODS: European regulators, HTA/payers, and other stakeholders (industry, academia, healthcare professionals, patient representatives) were invited by email to participate in a web-based survey. The survey was open between November 2023 and January 2024 and contained 24 questions including demographics and questions about perspectives toward registry-based data for decision-making purposes. The latter consisted of 5-point Likert scale items based on the theory of planned behavior (TPB), i.e., attitudes, subjective norm, perceived behavioral control, and intention. Descriptive analyses and a logistic regression analysis (outcome: intention; determinants: demographics, attitudes, subjective norm, behavioral control) were performed. RESULTS: Included were 191 respondents (response rate: 16%), of whom 110 were regulators (58%), 24 HTA/payers (13%), and 54 other stakeholders (28%). Most respondents were between 41 and 50 years old (32%), 65% were women, and 53% had > 10 years work experience. Respondents considered registry data in the medicinal product lifecycle most informative for characterization of disease epidemiology (mean 4.4; 95% confidence interval (CI) 4.2-4.5), and least informative for comparative effectiveness (mean 3.6; 95% CI 3.4-3.7). Reaching the relevant patient population was perceived as the biggest strength (mean 3.6; 95% CI 3.4-3.8), and data quality as the largest weakness of patient registries (mean 2.4; 95% CI 2.2-2.6). Compared with regulators, HTA/payers had a similar intention to use registry data (Odds ratio (OR) 1.56; 95% CI 0.47-5.16), while other stakeholders were more frequently very open (intention) to using registry data in the future (OR 8.48; 95% CI 3.00-23.98). Respondents from organizations in Northern Europe were less often very open to using registry data in the future than respondents from multinational organizations (OR 0.19; 95% CI 0.04-0.85). Finally, respondents with a high perceived behavioral control concerning the use of registry data were more often very open to using registry data in the future than respondents with a neutral or low perceived behavioral control (OR 3.45; 95% CI 1.37-8.64). CONCLUSIONS: The participants in our survey were generally open to increasing the use of registry data in the future. Nevertheless, perceived weaknesses such as data quality and accessibility will need to be addressed to align and improve stakeholders' perspectives on the use of patient registries as an evidence basis for medicines decision-making.
Are Causal Statements Reported in Pharmacovigilance Disproportionality Analyses Using Individual Case Safety Reports Exaggerated in Related Citations? A Meta-epidemiological Study.
Bernardeau C, Revol B, Salvo F … +5 more , Fusaroli M, Raschi E, Cracowski JL, Roustit M, Khouri C
Drug Saf · 2025 Jun · PMID 39987376 · Full text
BACKGROUND: Previous meta-epidemiological surveys have found considerable misinterpretation of results of disproportionality analyses. We aim to explore the relationship between the strength of causal statements used in...
BACKGROUND: Previous meta-epidemiological surveys have found considerable misinterpretation of results of disproportionality analyses. We aim to explore the relationship between the strength of causal statements used in title and abstract conclusions of pharmacovigilance disproportionality analyses and the strength of causal language used in citing studies. METHODS: On March 30, 2022, we selected the 30 disproportionality studies with the highest Altmetric Attention Scores. For each article, we extracted all citing studies using the Dimension database (n = 1434). In parallel, two authors assessed the strength of causal statements in the title and abstract conclusions of source articles and in the paragraph of citing studies. Based on previous studies, the strength of causal language was quantified based on a four-level scale (1-appropriate interpretation; 2-ambiguous interpretation; 3-conditionally causal; 4-unconditionally causal). Discrepancies were solved by discussion until consensus among the team. We assessed the association between the strength of causal statements in source articles and citing studies, separately for the title and abstract conclusions, through multinomial regression models. RESULTS: Overall, 27% (n = 8) of source studies used unconditionally causal statements in their title, 30% (n = 9) in their abstract conclusion, and 17% (n = 5) in both. Only 20% (n = 6) used appropriate statements in their title and in their abstract's conclusions. Among the 622 citing studies analyzed, 285 (45.8%) used unconditionally causal statements when referring to the findings from disproportionality analysis, and only 164 (26.4%) used appropriate language. Multinomial models found that the strength of causal statements in citing studies was positively associated with the strength of causal language used in abstract conclusions of source articles (Likelihood Ratio Test (LogLRT) p < 0.00001) but not in the titles. In particular, among studies citing source articles with appropriate interpretation, 30.2% (95% confidence interval [CI] 22.8-37.6) contained unconditionally causal statements in their abstract conclusions, versus 56.4% (95% CI 48.7-64.2) for studies citing source articles with unconditionally causal statements. CONCLUSIONS: Nearly half of the studies citing pharmacovigilance disproportionality analyses results used causal claims, particularly when the causal language used in the source article was stronger. There is a need for higher caution when writing, interpreting, and citing disproportionality studies.
Effectiveness of Transformer-Based Large Language Models in Identifying Adverse Drug Reaction Relations from Unstructured Discharge Summaries in Singapore.
Koon YL, Lam YT, Tan HX … +12 more , Teo DHC, Neo JW, Yap AJY, Ang PS, Loke CPW, Tham MY, Tan SH, Soh SLB, Foo BQP, Ling ZJ, Yip JLW, Dorajoo SR
Drug Saf · 2025 Jun · PMID 39982676 · Publisher ↗
INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancements in various text processing tasks. However, the applicability of t...
INTRODUCTION: Transformer-based large language models (LLMs) have transformed the field of natural language processing and led to significant advancements in various text processing tasks. However, the applicability of these LLMs in identifying related drug-adverse event (AE) pairs within clinical context may be limited by the prevalent use of non-standard sentence structures and grammar. METHOD: Nine transformer-based LLMs pre-trained on biomedical domain corpora are fine-tuned on annotated data (n = 5088) to classify drug-AE pairs in unstructured discharge summaries as causally related or unrelated. These LLMs are then validated on text segments from deidentified hospital discharge summaries from Singapore (n = 1647). To assess generalisability, the models are validated on annotated segments (n = 4418) from the Medical Information Mart for Intensive Care (MIMIC-III) database. Performance of LLMs in identifying related drug-AE pairs is then compared against a prior benchmark set by traditional machine learning models on the same data. RESULTS: Using an LLM-Bidirectional long short-term memory (LLM-BiLSTM) architecture, transformer-based LLMs improve F1 score as compared to prior benchmark with BioM-ELECTRA-Large-BiLSTM showing an average F1 score improvement of 16.1% (increase from 0.64 to 0.74). Applying additional rules on the LLM-based predictions, like ignoring drug-AE pairs when the AE is a known indication of the drug, results in a further reduction in false positive rates with precision increases of up to 5.6% (0.04 increment). CONCLUSION: Transformer-based LLMs outperform traditional machine learning methods in identifying causally related drug-AE pairs embedded within unstructured discharge summaries. Nonetheless the improvement in performance with rules indicates that LLMs still possess some degree of imperfection for this causal relation detection task.
Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel.
Wu L, Fang H, Qu Y … +2 more , Xu J, Tong W
Drug Saf · 2025 Jun · PMID 39979771 · Full text
BACKGROUND: Drug adverse events (AEs) represent a significant public health concern. US Food and Drug Administration (FDA) drug labeling documents are an essential resource for studying drug safety such as assessing a dr...
BACKGROUND: Drug adverse events (AEs) represent a significant public health concern. US Food and Drug Administration (FDA) drug labeling documents are an essential resource for studying drug safety such as assessing a drug's likelihood to cause certain organ toxicities; however, the manual extraction of AEs is labor-intensive, requires specialized expertise, and is challenging to maintain, due to frequent updates of the labeling documents. OBJECTIVE: To automate the extraction of AE data from FDA drug labeling documents, we developed a workflow based on AskFDALabel, a large language model (LLM)-powered framework, and its demonstration in drug safety studies. METHODS: This framework incorporates a retrieval-augmented generation (RAG) component based on FDALabel to enhance standard LLM inference. Key steps include (1) selection of a task-specific template, (2) FDALabel database querying, and (3) content preparation for LLM processing. We evaluated the performance of the framework in three benchmark experiments, including drug-induced liver injury (DILI) classification, drug-induced cardiotoxicity (DICT) classification, and AE term recognition. RESULTS: AskFDALabel achieved F1-scores of 0.978 for DILI, 0.931 for DICT, and 0.911 for AE annotation, outperforming other traditional methods. It also provided cited labeling content and detailed explanations, facilitating manual verification. CONCLUSION: AskFDALabel exhibited high consistency with human AE annotation, particularly in classifying and profiling DILI and DICT. Thus, it can significantly enhance the efficiency and accuracy of AE annotation, with promising potential for advanced AE surveillance and drug safety research.
Pharmacogenetic Testing in the Outpatient Setting in Switzerland: A Descriptive Study Using Swiss Claims Data.
Wittwer NL, Meier CR, Huber CA … +3 more , Meyer Zu Schwabedissen HE, Allemann S, Schneider C
Drug Saf · 2025 Jun · PMID 39976874 · Full text
BACKGROUND: In Switzerland, consumers are exposed to drugs with pharmacogenetic (PGx) recommendations in 78% of cases. Pre-emptive PGx testing for seven drugs (abacavir, carbamazepine, 6-mercaptopurine, azathioprine, 5-f...
BACKGROUND: In Switzerland, consumers are exposed to drugs with pharmacogenetic (PGx) recommendations in 78% of cases. Pre-emptive PGx testing for seven drugs (abacavir, carbamazepine, 6-mercaptopurine, azathioprine, 5-fluorouracil, capecitabine, and irinotecan) has been covered by basic health insurance since 2017. PGx testing for other drugs is only covered if it is reactive and prescribed by a clinical pharmacologist. No data are yet available on the implementation of PGx testing in the outpatient setting. AIM: The objective of this study was to determine the prevalence of ambulatory PGx testing in the Swiss population, to characterize PGx-tested individuals, and to identify the most commonly used drugs before and after PGx testing. METHODS: We assessed the prevalence of PGx testing in Switzerland and characterized individuals who underwent PGx testing between 2017 and 2021 using claims data from a large health insurance company. RESULTS: Of 894,748 individuals registered for the entire study period, only 817 (0.09%) underwent PGx testing. Those who underwent PGx testing were more frequently female and claimed more drugs and PGx drugs than those who did not undergo PGx testing. The drugs used before and after PGx testing differed, and fewer drugs with reimbursement for pre-emptive PGx testing were included before PGx testing. CONCLUSION: In Switzerland, personalized pharmacotherapy has the potential to be improved, as only 0.09% of the studied population underwent PGx testing, despite 77.4% claiming PGx drugs.
Aggregation of Adverse Event Terms for Signal Detection and Labeling in Clinical Trials.
Zink RC, Lyzinski R, Mann G
Drug Saf · 2025 Jun · PMID 39953273 · Publisher ↗
The Medical Dictionary for Regulatory Activities (MedDRA) was developed in the mid-to-late 1990s to address the shortcomings of other medical dictionaries used for coding adverse events. Since that time, MedDRA has becom...
The Medical Dictionary for Regulatory Activities (MedDRA) was developed in the mid-to-late 1990s to address the shortcomings of other medical dictionaries used for coding adverse events. Since that time, MedDRA has become the required coding dictionary for major regulatory authorities involved with the International Council for Harmonisation. Due to the increased specificity and significant increase in terminology over time, several approaches have been developed to aggregate terms for the purposes of signal detection and labeling. We present the approaches taken and suggested to date to aggregate preferred terms into meaningful medical concepts. We discuss the pros and cons of different methods in which to group terms, and illustrate that analyses performed for MedDRA preferred terms can also be conducted using aggregated terms. However, some features of adverse events available at the preferred term level, such as severity and relationship to study therapy, require additional consideration for analysis. In the last 25 years, the pendulum for medical coding is swinging in the other direction. Faced with a deluge of preferred terms, users of MedDRA are developing new ways in which to collapse terms into medical concepts. The ability to identify safety concerns and communicate important data in drug labels effectively and consistently are at risk, particularly with the introduction of new aggregations.
Assessment of Pharmacovigilance Across University Hospitals in Morocco.
Hamzaoui H, Shaum A, Cherkaoui I … +7 more , Moussa LA, Sefiani H, Talibi I, Benabdallah G, Salman O, Ferrey S, Soulaymani Bencheikh R
Drug Saf · 2025 May · PMID 39939518 · Full text
INTRODUCTION: Despite the increased scrutiny on vaccine safety following the coronavirus disease 2019 (COVID-19) pandemic, Morocco's Centre of Antipoison and Pharmacovigilance (CAPM) remained concerned that the pharmacov...
INTRODUCTION: Despite the increased scrutiny on vaccine safety following the coronavirus disease 2019 (COVID-19) pandemic, Morocco's Centre of Antipoison and Pharmacovigilance (CAPM) remained concerned that the pharmacovigilance system in Morocco was insufficiently implemented, including limited adverse event (AE) reporting, poor data use, and inconsistent training nationwide. OBJECTIVES: We sought to assess the status of pharmacovigilance activities (PAs) prior to formally institutionalizing them across university hospital centers (UHCs), given their position as the main providers of healthcare in Morocco and key sources for reporting serious AEs. METHODS: We assessed seven UHCs (housing 30 hospitals) in 2023 using a structured questionnaire with pharmacovigilance focal points developed from the World Health Organization's indicators of pharmacovigilance and the Global Benchmarking Tool. Data were grouped into 28 PAs and scored from 0 (not implemented) to 3 (fully implemented). We then calculated an implementation rate for each site on the basis of percent of PAs fully implemented (≥ 70%, well established; > 40% to < 70%, partially implemented; and ≤ 40%, not implemented). A desk review was also performed at the sites. Using the results of the assessment, three working groups of pharmacovigilance stakeholders developed recommendations to be formally adopted by UHCs. RESULTS: Basic elements of pharmacovigilance (notification forms and VigiFlow or Excel databases) were present at all the UHCs assessed. In total, 14 hospitals (47%) had well-established PAs, including advanced activities such as signal detection of adverse events following the use of medicines and vaccines, as well as causality assessment; 9 hospitals (30%) were partially implementing pharmacovigilance, and 7 hospitals (23%) had no established activities or very basic activities. Within four UHCs, activities had not been implemented at the same level from one hospital to another and vaccine vigilance was largely deprioritized. The working groups made recommendations for improving collaboration, standardizing procedures, and outlining a new organizational structure for pharmacovigilance, which was institutionalized by a formal agreement among UHCs in July 2023. CONCLUSIONS: The assessment revealed a subgroup of centers with well-established AE signal detection and causality assessment abilities, which could play a leading role in the country. After the site assessment, our collaborative approach of bringing together focal points to identify next steps and generate buy-in helped to formalize pharmacovigilance across centers.
Risk of Fracture Associated with Pregabalin or Mirogabalin Use: A Case-Case-Time-Control Study Based on Japanese Health Insurance Claims Data.
Wakabayashi H, Fukasawa T, Yoshida S … +4 more , Ri K, Masuda S, Anno T, Kawakami K
Drug Saf · 2025 May · PMID 39934585 · Publisher ↗
BACKGROUND: Pregabalin is widely used for neuropathic pain. Mirogabalin, a newer gabapentinoid, was recently introduced in several Asian countries. A previous case-crossover study showed an association between pregabalin...
BACKGROUND: Pregabalin is widely used for neuropathic pain. Mirogabalin, a newer gabapentinoid, was recently introduced in several Asian countries. A previous case-crossover study showed an association between pregabalin and fall-related injuries, but the findings may have been affected by biases from individual- and population-level exposure time trends, and generalizability was limited due to a focus on a middle-aged population. Further, no findings have appeared on mirogabalin and its association with fall-related fractures. OBJECTIVE: We conducted a case-case-time-control study to investigate the risk of fracture associated with pregabalin and mirogabalin in a population with broad demographic coverage. METHODS: Incident fractures were identified from a Japanese claims database. For each case, hazard (days 1-30 before the fracture event) and control windows (days 61-90 before the event) were set. Each current case was matched by age and sex to a future case, defined as a patient who experienced a fracture 120-365 days later. Odds ratios (ORs) were estimated using conditional logistic regression models. RESULTS: A total of 814,216 and 460,811 cases were included in the pregabalin and mirogabalin analyses, respectively. ORs were 1.35 (95% confidence interval 1.28-1.43) for pregabalin and 1.53 (1.35-1.72) for mirogabalin, indicating an increased risk of fracture with both drugs. These results remained consistent and robust across sensitivity and subgroup analyses, except in patients under 65 years of age. CONCLUSION: Given this observed risk and the fact that these medications are commonly prescribed to older populations, caution is warranted in clinical use.
Emerging Tools to Support DILI Assessment in Clinical Trials with Abnormal Baseline Serum Liver Tests or Pre-existing Liver Diseases.
Amirzadegan J, Tesfaldet B, Pei YV … +3 more , Navarro Almario E, Avigan MI, Hayashi PH
Drug Saf · 2025 May · PMID 39932652 · Full text
Based on the late Dr. Hyman Zimmerman's observation that hepatocellular drug-induced liver injury (DILI) leading to jaundice carries a ≥ 10% fatality risk (coined as Hy's law by others), evaluation of Drug-Induced Seriou...
Based on the late Dr. Hyman Zimmerman's observation that hepatocellular drug-induced liver injury (DILI) leading to jaundice carries a ≥ 10% fatality risk (coined as Hy's law by others), evaluation of Drug-Induced Serious Hepatotoxicity (eDISH) continues to play a central role in the assessment of a study drug's liability for acute hepatocellular DILI. The eDISH identifies drugs in clinical trials with DILI fatality (death or transplant) risk that may be unacceptable in a post-market setting. As a two-dimensional graph that plots peak total bilirubin (TB) versus peak serum aminotransferase levels for each patient during study drug or comparator treatment, eDISH identifies potential cases of acute, modest, and serious hepatocellular DILI for in-depth analysis of liver tests (LT) and clinical course so that the likelihood of causal association with the study drug can be determined. Unfortunately, the generalizable utility of this tool only pertains to trials enrolling patients with normal or near normal (NNN) baseline (BL) serum LTs. The eDISH does not necessarily apply to trials of patients with abnormal baseline (ABN-BL) LTs that often coincide with underlying liver disorders. Because drug development programs being reviewed by the FDA increasingly target liver disorders, we are often challenged to evaluate DILI risk in trials of patients with ABN-BL LTs. Also, the high background prevalence of metabolic dysfunction associated steatotic liver disease (MASLD) means patients with LTs above NNN may need to be enrolled in trials treating non-liver disorders to reflect the target population. Such study populations create challenges for industry and regulators because eDISH may not reliably categorize or identify potential cases of DILI for further analysis, as it so efficiently does in NNN-BL trials. We describe the main functionalities of eDISH in NNN-BL trials to understand what should be emulated by new tools or eDISH modifications. We then discuss non-eDISH-based plots that may be useful in ABN-BL trials.
Drug Induced Liver Injury: Highlights and Controversies in the 2023 Literature.
Singh H, Kunkle BF, Troia AR … +6 more , Suvarnakar AM, Waterman AC, Khin Y, Korkmaz SY, O'Connor CE, Lewis JH
Drug Saf · 2025 May · PMID 39921708 · Publisher ↗
Drug-induced liver injury (DILI) remains an active field of clinical research and investigation with more than 4700 publications appearing in 2023 relating to hepatotoxicity of all causes and injury patterns. As in years...
Drug-induced liver injury (DILI) remains an active field of clinical research and investigation with more than 4700 publications appearing in 2023 relating to hepatotoxicity of all causes and injury patterns. As in years past, we have attempted to identify and summarize highlights and controversies from the past year's literature. Several new and novel therapeutic agents were approved by the US Food and Drug Administration (FDA) in 2023, a number of which were associated with significant hepatotoxicity. Updates in the diagnosis and management of DILI using causality scores as well as newer artificial intelligence-based methods were published. Details of newly established hepatotoxins as well as updated information on previously documented hepatotoxic drugs is presented. Significant updates in treatment of DILI were also included as well as reports related to global DILI registries.
OpenPVSignal Knowledge Graph: Pharmacovigilance Signal Reports in a Computationally Exploitable FAIR Representation.
Chytas A, Gavriilides G, Kapetanakis A … +3 more , de Langlais A, Jaulent MC, Natsiavas P
Drug Saf · 2025 Apr · PMID 39921707 · Full text
INTRODUCTION: Pharmacovigilance signal report (PVSR) documents contain valuable condensed information published by drug monitoring organizations, typically in a free-text format. They provide initial insights into potent...
INTRODUCTION: Pharmacovigilance signal report (PVSR) documents contain valuable condensed information published by drug monitoring organizations, typically in a free-text format. They provide initial insights into potential links between drugs and harmful effects. Still, their unstructured format prevents this valuable information from being integrated into data-processing pipelines (e.g., to support either the investigation of drug safety signals or decision-making in the clinical context). OBJECTIVE: OpenPVSignal is a data model designed specifically to publish PVSRs via a computationally exploitable format, compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) principles to promote ease of access and reusability of these valuable data. METHODS: This paper outlines the procedure for converting pharmacovigilance signals published by the World Health Organization Uppsala Monitoring Centre (WHO-UMC) into the OpenPVSignal data model, resulting in a Knowledge Graph (KG). It details each step of the process, including the technical validation by KG engineers and the qualitative verification by medical and pharmacovigilance experts, leading to the finalized KG. RESULTS: A total of 101 PVSRs from 2011 to 2019 were incorporated into the openly available KG. CONCLUSION: The presented KG could be useful in various data-processing pipelines, including systems that support drug safety activities.
Network Analysis and Machine Learning for Signal Detection and Prioritization Using Electronic Healthcare Records and Administrative Databases: A Proof of Concept in Drug-Induced Acute Myocardial Infarction.
Barbieri MA, Abate A, Balogh OM … +10 more , Pétervári M, Ferdinandy P, Ágg B, Battini V, Cocco M, Rossi A, Carnovale C, Casula M, Spina E, Sessa M
Drug Saf · 2025 May · PMID 39918677 · Full text
BACKGROUND: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data fr...
BACKGROUND: Safety signals for potential drug-induced adverse events (AEs) typically emerge from multiple data sources, primarily spontaneous reporting systems, despite known limitations. Increasingly, real-world data from sources such as electronic health records (EHRs) and administrative databases are leveraged for signal detection. Although network analysis has shown promise in mapping relationships between clinical attributes for signal detection in spontaneous reporting system databases, its application in real-world data from EHRs and administrative databases remains limited. OBJECTIVE: This study aimed to evaluate the performance of network analysis in detecting safety signals within Italian administrative databases, using drug-induced acute myocardial infarction (AMI) as a proof of concept. METHODS: We employed a case-crossover design to explore the association between drug exposure and AMI using the Healthcare Administrative Database of Mantova, Italy, from 2014 to 2018. Patients with their first AMI hospitalization were identified after a 365-day washout period to exclude prior hospitalizations. We constructed a network to analyse the relationships between prescribed drugs and diagnoses, represented as nodes, with undirected edges illustrating their interactions. For each patient with AMI, we identified all diagnoses and drugs recorded or redeemed within 365 days of the first AMI episode and generated various drug-diagnosis, drug-drug, and diagnosis-diagnosis pairs. We calculated the frequency of these pairs, and three types of edge weights quantified the strength of connections. We identified outlier drug-AMI pairs using a predictive score (F) based on frequency (C) and full edge weights (W), with validation for known AMI associations. We prioritized signals using the F score, C of AMI, and W, analysed through k-means clustering to identify patterns in the data. RESULTS: From 2014 to 2018, a total of 3918 patients had an AMI, with 4686 AMI diagnoses. Of those, 2866 had prescriptions in the previous year, totalling 498,591 prescriptions. A network analysis identified 2968 unique nodes, revealing 529,935 diagnosis-diagnosis connections, 235,380 drug-diagnosis connections, and 102,831 drug-drug connections. The median number of connections (C) was 404 (Q1-Q3: 194-671) for drug nodes and 380 (Q1-Q3: 216-664) for diagnosis nodes. The median W was 11.8 (Q1-Q3: 9-14), and the median F score across pairs was 0.1 (Q1-Q3: 0.1-0.3). A total of 249 potential safety signals were detected, with 63.4% aligning with known AEs. Among the remaining signals, 80 were prioritized, and five emerged as the highest priority: terazosin, tamsulosin, allopurinol, esomeprazole, and omeprazole. CONCLUSIONS: Overall, our novel method demonstrates that network analysis is a valuable tool for signal detection and prioritization in drug-induced AEs based on EHRs and administrative databases.
A Longitudinal Post-authorization Safety Study of Golimumab in Treatment of Ulcerative Colitis: A Cohort Study in Denmark and Sweden, 2013-2021.
Ersbøll AK, Huang Z, Hill DD … +17 more , Hede SM, Andersen V, Bolin K, Kristensen MS, Esslinger S, Hansen FR, Hertervig E, Kallio L, Kjærulff TM, Kloster S, Krumme A, Lewis JD, Mehkri L, Qvist N, Thygesen LC, Weinstein C, Green A
Drug Saf · 2025 May · PMID 39913070 · Full text
BACKGROUND: When golimumab (GLM) was approved for the treatment of moderate to severe ulcerative colitis (UC) in 2013, a post-authorization safety study was conducted. OBJECTIVE: Our objective was to examine whether expo...
BACKGROUND: When golimumab (GLM) was approved for the treatment of moderate to severe ulcerative colitis (UC) in 2013, a post-authorization safety study was conducted. OBJECTIVE: Our objective was to examine whether exposure to GLM was associated with an increased incidence of all-cause total colectomy, colorectal cancer, and hepatosplenic T-cell lymphoma in Denmark and Sweden. METHODS: We conducted a new-user, active comparator cohort study of patients with UC in 2013-2021. Exposure to GLM, other anti-tumor necrosis factor (TNF) agents (infliximab and adalimumab) and thiopurines was a time-varying variable. Therapies were based on prescription redemptions and hospital-based administration of medications from national prescription and hospital registers. The association between exposure to study therapies and outcomes was evaluated using Poisson regression of incidence rates (IRs), presented as IR ratios (IRRs) and 95% confidence intervals (CIs). RESULTS: A total of 5177 and 7469 patients were included in Denmark and Sweden, respectively. The IR of all-cause total colectomy per 1000 person-years was higher in Denmark (IR 42.6; 95% CI 38.9-46.2) than in Sweden (IR 16.1; 95% CI 14.2-18.0). No significant difference was observed in all-cause total colectomy between GLM and other anti-TNF agents (Denmark: adjusted IRR [aIRR] 1.28; 95% CI 0.98-1.66; Sweden: aIRR 1.17; 95% CI 0.72-1.90). A significant difference was observed between GLM and thiopurines (Denmark: aIRR 13.62; 95% CI 8.73-21.26; Sweden: aIRR 4.52; 2.75-7.41). Privacy regulations prevented analysis of a few colorectal cancer events. No hepatosplenic T-cell lymphoma events were identified. CONCLUSION: The IR of all-cause total colectomy with GLM was similar to that with other anti-TNF agents but was much higher than with thiopurines, probably related to confounding by indication.
Delphi Method Consensus on Statistical Analysis and Reporting Recommendations for Single-Arm Pregnancy Medication Safety Studies Investigating Pregnancy, Birth and Neonatal Health Outcomes: A Contribution from IMI-ConcePTION.
Richardson JL, Moore A, Stellfeld M … +17 more , Geissbühler Y, Winterfeld U, Favre G, Chambers C, Beck E, Onken M, Dathe K, Ceulemans M, Diav-Citrin O, Shechtman S, Oliver AM, Hodson KK, Shiller DD, Alexe A, van Puijenbroek EP, Lewis DJ, Yates LM
Drug Saf · 2025 Jun · PMID 39907983 · Publisher ↗
BACKGROUND AND OBJECTIVE: Standardised procedures for performing and reporting safety monitoring studies investigating medications use in pregnancy may help improve data quality and the speed of data generation. The obje...
BACKGROUND AND OBJECTIVE: Standardised procedures for performing and reporting safety monitoring studies investigating medications use in pregnancy may help improve data quality and the speed of data generation. The objective of this study was to provide recommendations on the statistical analysis and reporting of single-arm pregnancy medication safety studies using primary source datasets. METHODS: A Delphi consensus-setting protocol was used to acquire agreement on recommendations from experts with extensive knowledge and experience in conducting studies investigating medication safety in pregnancy. A series of recommendations, along with their scientific justifications and examples of how to calculate and describe exposure and outcome incidences, were critiqued and improved through a series of online Delphi review rounds. Agreement to inclusion scoring was assessed using a five-point Likert scale. Recommendations with a median Likert-scale score of at least 4, where ≥ 80% of the expert panel scored the recommendation at level 4 or higher, was used as the threshold for inclusion. RESULTS: The Delphi consensus methodology produced a set of 30 recommendations spread over five themes. These included descriptions of (1) study sample, (2) medication exposure, (3) maternal outcomes, (4) pregnancy and birth outcomes, and (5) fetal and neonatal outcomes. Of the 30 recommendations, 19 were strongly advised while 11 were included for consideration where their implementation may be beneficial for supplementing data communication. CONCLUSION: Use of the finalised set of recommendations should be encouraged to help standardise published evidence around medication use in pregnancy.
Antiretroviral Use for HIV Prevention During Pregnancy: The Need to Strengthen Regulatory and Surveillance Systems in Africa.
Schaefer R, Donaldson L, Chigome A … +9 more , Escudeiro Dos Santos M, Lamprianou S, Ndembi N, Nwokike JI, Nyambayo P, Palmi V, Renaud F, Gonzalez Tome M, Miller V
Drug Saf · 2025 Mar · PMID 39873899 · Full text
HIV-prevention efforts focusing on women of child-bearing potential are needed to end the HIV epidemic in the African region. The use of antiretroviral drugs as pre-exposure prophylaxis (PrEP) is a critical HIV preventio...
HIV-prevention efforts focusing on women of child-bearing potential are needed to end the HIV epidemic in the African region. The use of antiretroviral drugs as pre-exposure prophylaxis (PrEP) is a critical HIV prevention tool. However, safety data on new antiretrovirals during pregnancy are often limited because pregnant people are excluded from drug development studies. Calls from communities, healthcare professionals, and regulators to improve the information supporting decision-making around the use of medical products during pregnancy have been increasing. Post-marketing safety surveillance is an essential tool for detecting adverse outcomes and evaluating real-world, longer-term effects of drugs. Detecting and evaluating uncommon pregnancy outcomes requires large sample sizes, highlighting the benefits of and need for safety surveillance. Surveillance systems vary widely across Africa, and the need for enhanced surveillance of PrEP use during pregnancy highlights the limitations of current regulatory and surveillance systems. Challenges include weak regulation and insufficient resources. Pooling of resources and regulatory harmonization could address resource challenges. The African Medicines Agency, as a specialized agency of the African Union, has the potential to improve African medical product regulation, including post-marketing safety surveillance. This can strengthen regulation and ensure that market authorization holders meet their responsibility to invest in post-marketing surveillance systems, such as pregnancy registries. At the same time, independent post-marketing studies are needed to ensure generation of essential safety data. The Forum for Collaborative Research has initiated a project to facilitate interactions between regulators in Africa, the USA, and Europe, as well as other stakeholders, and to work toward consensus on safety data generation from PrEP during pregnancy before and after marketing authorization.
Sequential Epidemiological Analyses of Real-World Data: A Tool for Prospective Drug Safety Surveillance from the Rofecoxib Example.
Abbasi SH, Lund LC, Hallas J … +1 more , Pottegård A
Drug Saf · 2025 May · PMID 39869300 · Full text
INTRODUCTION: Large administrative healthcare databases can be used for near real-time sequential safety surveillance of drugs as an alternative approach to traditional reporting-based pharmacovigilance. The study aims t...
INTRODUCTION: Large administrative healthcare databases can be used for near real-time sequential safety surveillance of drugs as an alternative approach to traditional reporting-based pharmacovigilance. The study aims to build and empirically test a prospective drug safety monitoring setup and perform a sequential safety monitoring of rofecoxib use and risk of cardiovascular outcomes. METHODS: We used Danish population-based health registers and performed sequential analysis of rofecoxib use and cardiovascular outcomes using case-time-control and cohort study designs from January 2000 to September 2004. Each monitoring period added 6 months of data until the end of the study period. In the case-time-control study, incident cases of myocardial infarction (MI) and ischemic stroke were identified and matched with up to five time controls on age, sex, and calendar time. Exposure status on the date of diagnosis was assessed using a 60-day focal window, with reference windows 120, 180, and 240 days prior to the diagnoses. In the cohort study, incident users of rofecoxib were matched up to 1:4 with ibuprofen users (active comparators) using high-dimensional disease risk scores and were followed for 60 days. RESULTS: The earliest association between rofecoxib use and the risk of MI was seen in study period 2 for case-time-control design (OR 1.42, 95% CI 1.04-1.93) and in study period 7 for the cohort study design (RR 1.22; 95% CI 1.02-1.47). CONCLUSIONS: Our prospective drug safety monitoring setup showed that the risk of MI could have been detected 3.5 years before the ultimate market withdrawal of rofecoxib. However, further research is needed to validate this approach.
Empowering African Expertise: Enhancing Safety Data Integration and Signal Detection for COVID-19 Vaccines Through the African Union Smart Safety Surveillance Joint Signal Management Group.
Nambasa VP, Gunter HM, Adeyemo MB … +32 more , Bhawaneedin NY, Blockman M, Sabblah GT, Gyapong JO, Guantai EM, Abebe T, Abebe W, Lawson HJ, Leburu MC, Mohammed A, Amponsa-Achiano K, Matlala MF, Elemuwa UG, Mogtari H, Nyarko AK, Schönfeldt M, Kamupira M, McCarthy K, Tefera YL, Alemu A, Yusuf KM, Emelife O, Sidibe L, Dandajena K, Onu K, Adeyeye MC, Darko DM, Gerba H, Semete B, Siyoi F, Ambali A, Meyer JC
Drug Saf · 2025 Mar · PMID 39843797 · Full text
INTRODUCTION: The COVID-19 pandemic accelerated new vaccine development. Limited safety data necessitated robust global safety surveillance to accurately identify and promptly communicate potential safety issues. The Afr...
INTRODUCTION: The COVID-19 pandemic accelerated new vaccine development. Limited safety data necessitated robust global safety surveillance to accurately identify and promptly communicate potential safety issues. The African Union Smart Safety Surveillance (AU-3S) program established the Joint Signal Management (JSM) group to support identification of potential vaccine safety concerns in five pilot countries (Ethiopia, Ghana, Kenya, Nigeria, South Africa), accounting for approximately 35% of the African population. OBJECTIVE: Our objective was to provide an overview of the JSM group's role in supporting signal management activities for the AU-3S program during the COVID-19 pandemic. METHODS: Spontaneous, electronically reported COVID-19 vaccine adverse events following immunization (AEFI) from each country's safety data were integrated into the interim Data Integration and Signal Detection system. Statistical disproportionality methods were used to identify and review vaccine-event combinations (VECs) for potential safety concerns. The JSM group-which comprised pharmacovigilance and subject matter experts from National Medicine Regulatory Authorities, Expanded Programs on Immunization, and vaccine safety committees-conducted signal detection activities on cross-country safety data and provided recommendations. RESULTS: From April 2021 to December 2023, a total of 48,294 spontaneously reported AEFI were analyzed for six COVID-19 vaccines (NRVV Ad [ChAdOx1 nCoV-19]; Ad26.COV2.S; Elasomeran; Tozinameran; Covid-19 vaccine [Vero Cell], Inactivated; NRVV Ad26 [Gam-Covid-Vac]) administered in Ethiopia (34.6%), Nigeria (30.3%), South Africa (16.9%), Ghana (13.5%), and Kenya (4.7%). Overall, 2,742 VECs were validated. A causal association between the COVID-19 vaccines and the reported AEFI cannot be inferred, as data were reported spontaneously. JSM group recommendations included monitoring for further evidence, no immediate action required, engaging marketing authorization holder(s) for additional information, or sensitizing healthcare providers and/or the public about events. Although no new safety signals were identified, nine safety-related recommendations were issued, including patient and healthcare provider education. CONCLUSIONS: The JSM group established a scalable and replicable model for future signal management of other priority health products in low- and middle-income countries, fostering ongoing collaboration and capacity building. Knowledge and experience gained from this pilot initiative will guide stakeholders in future safety surveillance initiatives within the African continent.
Clinical Relatedness and Stability of vigiVec Semantic Vector Representations of Adverse Events and Drugs in Pharmacovigilance.
Erlanson N, China JF, Taavola H … +1 more , Norén GN
Drug Saf · 2025 Apr · PMID 39833656 · Full text
INTRODUCTION: Individual case reports are essential to identify and assess previously unknown adverse effects of medicines. On these reports, information on adverse events (AEs) and drugs are encoded in hierarchical term...
INTRODUCTION: Individual case reports are essential to identify and assess previously unknown adverse effects of medicines. On these reports, information on adverse events (AEs) and drugs are encoded in hierarchical terminologies. Encoding differences may hinder the retrieval and analysis of clinically related reports relevant to a topic of interest. Recent studies have explored the use of data-driven semantic vector representations to support analysis of pharmacovigilance data. OBJECTIVE: This study aims to evaluate the stability and clinical relatedness of vigiVec, a semantic vector representation for codes of AEs and drugs. METHODS: vigiVec is a published adaptation to pharmacovigilance of the publicly available Word2Vec model, applied to structured data instead of free text. It provides vector representations for MedDRA Preferred Terms and WHODrug Global active ingredients, learned from reporting patterns in VigiBase, the WHO global database of adverse event reports for medicines and vaccines. For this study, a 20-dimensional Skip-gram architecture with window size 250 was used. Our evaluation focused on nearest neighbors identified by the cosine similarity of vigiVec vector representations. Clinical relatedness was measured through term intruder detection, whereby a medical doctor was tasked to identify a randomly selected term-the intruder-included among the four nearest neighbors to a specific AE or drug. Stability was measured as the average overlap in the ten nearest neighbors for each AE or drug, in repeated fittings of vigiVec. RESULTS: Among the ten nearest neighbors, 1.8 AEs on average belonged to the same MedDRA High Level Term (HLT; e.g., coagulopathies), and 1.3 drugs belonged to the same Anatomical Therapeutic Chemical level 3 (ATC-3; e.g., opioids). In the intruder detection task, when neighbors and intruders were both chosen from the same HLT, the intruder detection rate was 46%. When selected from different HLTs, it was 79%. By random chance, we should expect 20% (1 in 5). Corresponding rates for drugs were 42% in same ATC-3 and 65% in different ATC-3. The stability of nearest neighbors was 80% for AEs and 64% for drugs. CONCLUSION: Nearest neighbors identified with vigiVec are stable and show high level of clinical relatedness. They are often from different parts of the existing hierarchies and complement these.
Multi-Stakeholder Call to Action for the Future of Vaccine Post-Marketing Monitoring: Proceedings from the First Beyond COVID-19 Monitoring Excellence (BeCOME) Conference.
Bauchau V, Bollaerts K, Bryan P … +26 more , Buttery J, Davis K, Chen RT, Feikin DR, Fretta A, Frise S, Gandhi-Banga S, Izurieta HS, Jouquelet-Royer C, Khromava A, Li L, Long R, MacDonald S, Marcelon L, Massouh R, Meeraus W, Munoz FM, Naim K, Nordenberg D, Nohynek H, Rubino H, Salmon DA, Sellers S, Serradell L, Torcel-Pagnon L, Wilkins J
Drug Saf · 2025 May · PMID 39792303 · Full text
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Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.
Golder S, Xu D, O'Connor K … +3 more , Wang Y, Batra M, Hernandez GG
Drug Saf · 2025 Apr · PMID 39786481 · Full text
BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmac...
BACKGROUND: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear. OBJECTIVE: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources. METHODS: A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts. RESULTS: Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice. CONCLUSIONS: Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.
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